Ancestry and somatic profile indicate acral melanoma origin and prognosis - Nature

Analysis of the somatic and transcriptomic profile of 123 acral melanoma samples from Mexican patients helps understand tumour origins and prognosis, and highlights the importance of including samples...

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Leveraging data as a patient–scientist: frustrations and opportunities - Nature Reviews Nephrology

The transition from data scientist to patient–scientist has given me new perspectives into clinical research and strengthened my commitment to open science. Although limitations on data availability h...

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Accurate sample deconvolution of pooled snRNA-seq using sex-dependent gene expression patterns

Single-nucleus RNA sequencing (snRNA-seq) technology offers unprecedented resolution for studying cell type-specific gene expression patterns. However, snRNA-seq poses high costs and technical limitations, often requiring the pooling of independent biological samples and the loss of individual sample-level data. Deconvolution of sample identity using inherent features would enable the incorporation of pooled barcoding and sequencing protocols, thereby increasing data throughput and analytical sample size without requiring increases in experimental sample size and sequencing costs. In this study, we demonstrate a proof of concept that sex-dependent gene expression patterns can be leveraged for the deconvolution of pooled snRNA-seq data. Using previously published snRNA-seq data from the rat ventral tegmental area, we trained a range of machine learning models to classify cell sex using genes differentially expressed in cells from male and female rats. Models that used sex-dependent gene expression predicted cell sex with high accuracy (93-95%) and outperformed simple classification models using only sex chromosome gene expression (88-90%). The generalizability of these models to other brain regions was assessed using an additional published data set from the rat nucleus accumbens. Within this data set, model performance remained highly accurate in cell sex classification (90-92% accuracy) with no additional training. This work provides a model for future snRNA-seq studies to perform sample deconvolution using a two-sex pooled sample sequencing design and benchmarks the performance of various machine learning approaches to deconvolve sample identification from inherent sample features. ### Competing Interest Statement The authors have declared no competing interest. National Institute on Drug Abuse, DP1DA039650, R01DA053743, R01DA054714 National Institute of Mental Health, https://ror.org/04xeg9z08, R01MH114990 McKnight Foundation, https://ror.org/003ghvj67, Neurobiology of Brain Disorders Award

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ドラマ『コウノドリ』より　BABY, GOD BLESS YOU　ORCHESTRA POSSIBLE

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